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A decision classifier to classify rivers for river management based on their structure in China: an example from the Yongding river

机译:基于河流结构的河流分类决策决策器-以永定河为例

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摘要

River classification is a very useful tool for river management yet still a difficult task. This paper proposed a new decision classifier (DCF) to classify rivers for Chinese river management based on existing classification systems. Aimed at river function management, the DCF with the five-layers frame was developed on reach level in a spatially nested pattern that from top to bottom are natural province, basin, valley, reach, habitat and microhabitat. Five indexes (artificial degree, closeness, sinuosity, bed material texture, geomorphic units (GUs)) were selected and organized into the DCF according to the importance of the influence on river structure from macro to micro, large to small and top to bottom, because they represent main aspects of river structures and are easy to obtain. In addition, the closeness index is another good connector between valley level and reach level, and the GUs index links reach level to habitat level. The overall procedure to use DCF includes primary indoor classification and field validation. Remote sensing, geographical information system and global positioning system technologies were adopted in the process to dramatically reduce workload, especially fieldwork. Finally, the approach was applied to the Yongding river as a good example, and 17 river styles were identified.
机译:河流分类是河流管理的非常有用的工具,但仍然是一项艰巨的任务。本文提出了一种新的决策分类器(DCF),用于基于现有分类系统对中国河流管理的河流进行分类。针对河流功能管理,具有五层框架的DCF在到达水平上以空间嵌套的模式开发,从上到下是自然省,盆地,山谷,河段,生境和微生境。根据对河流结构影响的重要性,从宏观到微观,从大到小,从上到下,选择了五个指标(人为程度,紧密度,波纹度,床身质地,地貌单位(GUs))并将其组织到DCF中,因为它们代表了河流结构的主要方面,而且很容易获得。此外,紧密度指数是山谷水平和到达水平之间的另一个很好的联系者,GUs指数链接了到达水平到栖息地水平。使用DCF的整个过程包括主要的室内分类和现场验证。在这一过程中采用了遥感,地理信息系统和全球定位系统技术,以大大减少工作量,特别是实地工作。最后,将该方法应用于永定河作为一个很好的例子,并确定了17种河流样式。

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